BAYESIAN METHODS FOR COMPLETING DATA IN SPACE-TIME PANEL MODELS
Completing data sets that are collected in heterogeneous units is a quite frequent
problem. Chow and Lin (1971) were the rst to develop a unied framework for
the three problems (interpolation, extrapolation and distribution) of predicting
times series by related series (the `indicators'). This paper develops a spatial
Chow-Lin procedure for cross-sectional and panel data and compares the classical
and Bayesian estimation methods. We outline the error covariance structure
in a spatial context and derive the BLUE for the ML and Bayesian MCMC estimation.
Finally, we apply the procedure to Spanish regional GDP data between
2000-2004. We assume that only NUTS-2 GDP is known and predict GDP
at NUTS-3 level by using socio-economic and spatial information available at
NUTS-3. The spatial neighborhood is dened by either km distance, travel time,
contiguity and trade relationships. After running some sensitivity analysis, we
present the forecast accuracy criteria comparing the predicted values with the
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